{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Untitled12.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "nx_Zyvu2e3T1",
        "colab_type": "code",
        "outputId": "a42c19c7-8727-4fa9-b3c1-3107c781800b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 381
        }
      },
      "source": [
        "!pip install tensorflow==1.12.0\n",
        "!pip install coremltools"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: tensorflow==1.12.0 in /usr/local/lib/python3.6/dist-packages (1.12.0)\n",
            "Requirement already satisfied: protobuf>=3.6.1 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (3.10.0)\n",
            "Requirement already satisfied: absl-py>=0.1.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.9.0)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.27.1)\n",
            "Requirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.17.5)\n",
            "Requirement already satisfied: keras-applications>=1.0.6 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.0.8)\n",
            "Requirement already satisfied: keras-preprocessing>=1.0.5 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.1.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.12.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.34.2)\n",
            "Requirement already satisfied: astor>=0.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.8.1)\n",
            "Requirement already satisfied: gast>=0.2.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (0.2.2)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.1.0)\n",
            "Requirement already satisfied: tensorboard<1.13.0,>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tensorflow==1.12.0) (1.12.2)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.6.1->tensorflow==1.12.0) (45.2.0)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.6/dist-packages (from keras-applications>=1.0.6->tensorflow==1.12.0) (2.8.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow==1.12.0) (3.2.1)\n",
            "Requirement already satisfied: werkzeug>=0.11.10 in /usr/local/lib/python3.6/dist-packages (from tensorboard<1.13.0,>=1.12.0->tensorflow==1.12.0) (1.0.0)\n",
            "Requirement already satisfied: coremltools in /usr/local/lib/python3.6/dist-packages (3.3)\n",
            "Requirement already satisfied: numpy>=1.14.5 in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.17.5)\n",
            "Requirement already satisfied: protobuf>=3.1.0 in /usr/local/lib/python3.6/dist-packages (from coremltools) (3.10.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from coremltools) (1.12.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from protobuf>=3.1.0->coremltools) (45.2.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N7M-1RR-yP5y",
        "colab_type": "code",
        "outputId": "b92bea96-16a5-4be0-e484-24a0018100bf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:523: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint8 = np.dtype([(\"qint8\", np.int8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:524: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint8 = np.dtype([(\"quint8\", np.uint8, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:525: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint16 = np.dtype([(\"qint16\", np.int16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:526: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_quint16 = np.dtype([(\"quint16\", np.uint16, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:527: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  _np_qint32 = np.dtype([(\"qint32\", np.int32, 1)])\n",
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/dtypes.py:532: FutureWarning: Passing (type, 1) or '1type' as a synonym of type is deprecated; in a future version of numpy, it will be understood as (type, (1,)) / '(1,)type'.\n",
            "  np_resource = np.dtype([(\"resource\", np.ubyte, 1)])\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<p style=\"color: red;\">\n",
              "The default version of TensorFlow in Colab will soon switch to TensorFlow 2.x.<br>\n",
              "We recommend you <a href=\"https://www.tensorflow.org/guide/migrate\" target=\"_blank\">upgrade</a> now \n",
              "or ensure your notebook will continue to use TensorFlow 1.x via the <code>%tensorflow_version 1.x</code> magic:\n",
              "<a href=\"https://colab.research.google.com/notebooks/tensorflow_version.ipynb\" target=\"_blank\">more info</a>.</p>\n"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "1.12.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dvPyeQUqfWfO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 51
        },
        "outputId": "25d624c0-2dfc-4edf-b17c-8a560bed4517"
      },
      "source": [
        "import tensorflow as tf\n",
        "import keras\n",
        "from keras.models import Sequential, Model\n",
        "from keras.layers import Dense, Flatten, Input, Embedding, Flatten, concatenate\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import preprocessing\n",
        "import coremltools"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Using TensorFlow backend.\n",
            "WARNING:root:Keras version 2.2.5 detected. Last version known to be fully compatible of Keras is 2.2.4 .\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nSthi-VISSNi",
        "colab_type": "text"
      },
      "source": [
        "# Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i1n4RkrZANQW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Swift for TensorFlow Model\n",
        "\n",
        "# struct RegressionModel: Module {\n",
        "#     var numericalLayer = Dense<Float>(inputSize: 11, outputSize: 32, activation: relu)\n",
        "#     var embedding1 = Embedding<Float>(vocabularySize: 2, embeddingSize: 2)\n",
        "#     var embedding2 = Embedding<Float>(vocabularySize: 9, embeddingSize: 5)\n",
        "#     var embeddingLayer = Dense<Float>(inputSize: (4 + 45), outputSize: 64, activation: relu)\n",
        "#     var allInputConcatLayer = Dense<Float>(inputSize: (32 + 64), outputSize: 128, activation: relu)\n",
        "#     var hiddenLayer = Dense<Float>(inputSize: 128, outputSize: 32, activation: relu)\n",
        "#     var outputLayer = Dense<Float>(inputSize: 32, outputSize: 1)\n",
        "    \n",
        "#     @differentiable\n",
        "#     func callAsFunction(_ input: MultiInputs<[Tensor<Float>], [Tensor<Int32>]>) -> Tensor<Float> {\n",
        "#         let numericalInput = numericalLayer(input.numerical[0])\n",
        "#         let embeddingOutput1 = embedding1(input.categorical[0])\n",
        "#         let embeddingOutput1Reshaped = embeddingOutput1.reshaped(to: \n",
        "#             TensorShape([embeddingOutput1.shape[0], embeddingOutput1.shape[1] * embeddingOutput1.shape[2]]))\n",
        "#         let embeddingOutput2 = embedding2(input.categorical[1])\n",
        "#         let embeddingOutput2Reshaped = embeddingOutput2.reshaped(to: \n",
        "#             TensorShape([embeddingOutput2.shape[0], embeddingOutput2.shape[1] * embeddingOutput2.shape[2]]))\n",
        "#         let embeddingConcat = Tensor<Float>(concatenating: [embeddingOutput1Reshaped, embeddingOutput2Reshaped], alongAxis: 1)\n",
        "#         let embeddingInput = embeddingLayer(embeddingConcat)\n",
        "#         let allConcat = Tensor<Float>(concatenating: [numericalInput, embeddingInput], alongAxis: 1)\n",
        "#         return allConcat.sequenced(through: allInputConcatLayer, hiddenLayer, outputLayer)\n",
        "#     }\n",
        "# }"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JXp-AqomffLx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "numerical_input = Input(shape=(11,))\n",
        "categorical_input1 = Input(shape=(1,))\n",
        "categorical_input2 = Input(shape=(1,))\n",
        "\n",
        "numerical = Dense(16, activation='relu')(numerical_input)\n",
        "\n",
        "embedding1 = Embedding(input_dim=2, output_dim=2, input_length=1)(categorical_input1)\n",
        "embedding1 = Flatten()(embedding1)\n",
        "\n",
        "embedding2 = Embedding(input_dim=9, output_dim=5, input_length=1)(categorical_input2)\n",
        "embedding2 = Flatten()(embedding2)\n",
        "\n",
        "embeddingConcat = concatenate([embedding1, embedding2], axis=1)\n",
        "embedding = Dense(16, activation='relu')(embeddingConcat)\n",
        "\n",
        "allConcat = concatenate([numerical, embedding], axis=1)\n",
        "hidden = Dense(64, activation='relu')(allConcat)\n",
        "hidden = Dense(32, activation='relu')(hidden)\n",
        "output = Dense(1)(hidden)\n",
        "\n",
        "keras_model = Model(inputs=[numerical_input, categorical_input1, categorical_input2], outputs=output)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SePBabhfk5D",
        "colab_type": "code",
        "outputId": "8a5ae072-334c-437f-82d6-ce6a612e46a6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        }
      },
      "source": [
        "keras_model.summary()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model_1\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_2 (InputLayer)            (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "input_3 (InputLayer)            (None, 1)            0                                            \n",
            "__________________________________________________________________________________________________\n",
            "embedding_1 (Embedding)         (None, 1, 2)         4           input_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "embedding_2 (Embedding)         (None, 1, 5)         45          input_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "flatten_1 (Flatten)             (None, 2)            0           embedding_1[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "flatten_2 (Flatten)             (None, 5)            0           embedding_2[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "input_1 (InputLayer)            (None, 11)           0                                            \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 7)            0           flatten_1[0][0]                  \n",
            "                                                                 flatten_2[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 16)           192         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 16)           128         concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_2 (Concatenate)     (None, 32)           0           dense_1[0][0]                    \n",
            "                                                                 dense_2[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_3 (Dense)                 (None, 64)           2112        concatenate_2[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "dense_4 (Dense)                 (None, 32)           2080        dense_3[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_5 (Dense)                 (None, 1)            33          dense_4[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 4,594\n",
            "Trainable params: 4,594\n",
            "Non-trainable params: 0\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sfxSyK6xSLsu",
        "colab_type": "text"
      },
      "source": [
        "# Save and Export no Train Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "k8SQOLPlfoxX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "keras_model.save('keras_no_train_model.h5')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MkXmhvweF1ti",
        "colab_type": "code",
        "outputId": "6025707e-5ec8-4c6a-8d30-bd58ebe0d277",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "source": [
        "mlmodel = coremltools.converters.keras.convert('keras_no_train_model.h5', use_float_arraytype = True)\n",
        "mlmodel.save('coreml_no_train.mlmodel')"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0 : input_2, <keras.engine.input_layer.InputLayer object at 0x7f947f87a438>\n",
            "1 : input_3, <keras.engine.input_layer.InputLayer object at 0x7f947f87a4a8>\n",
            "2 : embedding_1, <keras.layers.embeddings.Embedding object at 0x7f947f87a550>\n",
            "3 : embedding_2, <keras.layers.embeddings.Embedding object at 0x7f947f87a588>\n",
            "4 : flatten_1, <keras.layers.core.Flatten object at 0x7f947f87a518>\n",
            "5 : flatten_2, <keras.layers.core.Flatten object at 0x7f947f87a5c0>\n",
            "6 : input_1, <keras.engine.input_layer.InputLayer object at 0x7f947f87a6a0>\n",
            "7 : concatenate_1, <keras.layers.merge.Concatenate object at 0x7f947f87a630>\n",
            "8 : dense_1, <keras.layers.core.Dense object at 0x7f947f87aac8>\n",
            "9 : dense_1__activation__, <keras.layers.core.Activation object at 0x7f947ef0c908>\n",
            "10 : dense_2, <keras.layers.core.Dense object at 0x7f947f87a7b8>\n",
            "11 : dense_2__activation__, <keras.layers.core.Activation object at 0x7f947ef0c3c8>\n",
            "12 : concatenate_2, <keras.layers.merge.Concatenate object at 0x7f947f87a940>\n",
            "13 : dense_3, <keras.layers.core.Dense object at 0x7f947f87ab70>\n",
            "14 : dense_3__activation__, <keras.layers.core.Activation object at 0x7f947f8743c8>\n",
            "15 : dense_4, <keras.layers.core.Dense object at 0x7f947f87ab00>\n",
            "16 : dense_4__activation__, <keras.layers.core.Activation object at 0x7f947f874588>\n",
            "17 : dense_5, <keras.layers.core.Dense object at 0x7f947f87acc0>\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py:310: UserWarning: No training configuration found in save file: the model was *not* compiled. Compile it manually.\n",
            "  warnings.warn('No training configuration found in save file: '\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fsek-y7o0_FM",
        "colab_type": "code",
        "outputId": "cc746cb8-f939-4443-9123-91f46ee89a0f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "spec = mlmodel.get_spec()\n",
        "spec"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "specificationVersion: 1\n",
              "description {\n",
              "  input {\n",
              "    name: \"input1\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 11\n",
              "        dataType: FLOAT32\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  input {\n",
              "    name: \"input2\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 1\n",
              "        dataType: FLOAT32\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  input {\n",
              "    name: \"input3\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 1\n",
              "        dataType: FLOAT32\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  output {\n",
              "    name: \"output1\"\n",
              "    type {\n",
              "      multiArrayType {\n",
              "        shape: 1\n",
              "        dataType: FLOAT32\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  metadata {\n",
              "    userDefined {\n",
              "      key: \"coremltoolsVersion\"\n",
              "      value: \"3.3\"\n",
              "    }\n",
              "  }\n",
              "}\n",
              "neuralNetwork {\n",
              "  layers {\n",
              "    name: \"embedding_1\"\n",
              "    input: \"input2\"\n",
              "    output: \"embedding_1_output\"\n",
              "    embedding {\n",
              "      inputDim: 2\n",
              "      outputChannels: 2\n",
              "      weights {\n",
              "        floatValue: 0.03467612341046333\n",
              "        floatValue: -0.04324455186724663\n",
              "        floatValue: 0.01923951879143715\n",
              "        floatValue: -0.03662766143679619\n",
              "      }\n",
              "    }\n",
              "  }\n",
              "  layers {\n",
              "    name: \"embedding_2\"\n",
              "    input: \"input3\"\n",
              "    output: \"embedding_2_output\"\n",
              "    embedding {\n",
              "      inputDim: 9\n",
              "      outputChannels: 5\n",
              "      weights {\n",
              "        floatValue: -0.011730790138244629\n",
              "        floatValue: -0.021260643377900124\n",
              "        floatValue: 0.038585517555475235\n",
              "        floatValue: 0.010404326021671295\n",
              "        floatValue: -0.04791774973273277\n",
              "        floatValue: -0.02666456811130047\n",
              "        floatValue: 0.025882039219141006\n",
              "        floatValue: 0.04379313066601753\n",
              "        floatValue: 0.021602127701044083\n",
              "        floatValue: 0.04311491176486015\n",
              "        floatValue: 0.028478238731622696\n",
              "        floatValue: 0.03834667429327965\n",
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              "        floatValue: -0.039838552474975586\n",
              "        floatValue: -0.04846785217523575\n",
              "        floatValue: -0.006758011877536774\n",
              "        floatValue: 0.04574122652411461\n",
              "        floatValue: -0.031536877155303955\n",
              "        floatValue: 0.006875813007354736\n",
              "        floatValue: 0.02204456552863121\n",
              "        floatValue: -0.036993883550167084\n",
              "        floatValue: 0.045613911002874374\n",
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            "tags": []
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}